All the articles I've archived.
A complete derivation of backpropagation for MLPs — from chain rule intuition to delta propagation, with a worked numerical example showing exactly how errors flow backward through a network.
From modular stacks to unified intelligence: How foundation models are reshaping AV and generalist robotics. Covers VLA models (GR00T, Pi0), Physical AI, and the 2026 embodied revolution.
A comprehensive guide to Python interview hacks, advanced patterns, tricky syntax, and gotchas that separate strong candidates from elite ones. Covers heapq, DP, bitwise operations, monotonic stacks, and more.
Pre-training gives models capability; post-training gives them value. A deep dive into LoRA, DoRA, DPO, and how we sculpt intelligence after the initial birth.
From naive vector search to industry-standard multimodal RAG. Master hybrid search, query rewriting, cross-encoder reranking, and the architecture of high-precision retrieval systems.
From stateless inference to tool-augmented AI agents. Learn how the Model Context Protocol (MCP), secure sandboxes, and holistic versioning enable the next generation of AI systems.
Standard MLOps advice tells you to learn Git and Docker. But for the next generation of AI Engineers, that's just the baseline. This roadmap focuses on the Infrastructure Round—deep-diving into how data is structured for speed, how it's fed into models, how those models scale across clusters, and how we squeeze every drop of performance out of the silicon.
The unsung hero of modern data processing is how we structure data itself. Learn how Apache Parquet and Apache Arrow solve the fundamental trade-off between storage efficiency and compute speed.
A narrative-first walkthrough of reinforcement learning, starting with everyday intuition and ending with the math behind Q-learning and DQN.
Why modern AI teams are handcrafting GPU kernels—from FlashAttention to Triton code—and how silicon-level tuning is the new frontier of MLOps.
A structured articulation and pacing warm-up designed to help technologists speak with clarity and confidence in high-stakes meetings.
How PagedAttention, Continuous Batching, Speculative Decoding, and Quantization unlock lightning-fast, reliable large language model serving.
A high level view on how modern vision-language models connect pixels and prose, from CLIP and BLIP to Flamingo, MiniGPT-4, Kosmos, and Gemini.
A guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to 3D parallelism and fault tolerance.
A comprehensive deep-dive into production inference optimization, tracing the path of a request through LLM and diffusion model serving systems. Understanding the bottlenecks from gateway to GPU kernel execution.
A collaborative 45-minute thinking algorithm tuned for Google-style coding interviews—classify the problem, co-design an optimal approach, code with confidence, and handle follow-ups with ease.
A deep dive into XGBoost — how second-order Taylor approximations and sophisticated regularization make it the dominant algorithm for structured data, bridging mathematical rigor with system engineering excellence.
How diffusion models predict action sequences instead of pixels. Covers Diffusion Policy, world models for robotics, and connecting diffusion to reinforcement learning for autonomous systems.
The evolution of image diffusion architectures. Learn how we moved from convolutional U-Nets to scalable Diffusion Transformers (DiT), and why treating images like language changed everything.
Exploring the state-of-the-art in video generation. Learn how Sora and Veo use Spatiotemporal Transformers to simulate the physical world, and the challenges of achieving perfect motion fidelity.
How to train a world-class diffusion model. Covers the complete lifecycle: from large-scale pre-training on noisy web data to specialized post-training, alignment, and aesthetic fine-tuning.
How to accelerate diffusion sampling and steer creativity. Learn the mechanics of DDIM, DPM-Solver, Classifier-Free Guidance (CFG), and the math of negative prompting.
The fundamentals of video diffusion models. Learn how we extend 2D diffusion to time, the mechanics of temporal attention, and the architectural shifts required for motion consistency.
A deep dive into how datasets and dataloaders power modern AI. Understanding the architectural shift from Python row-loops to C++ zero-copy data pumps.
Why L5 autonomy is harder than a moon landing. Understanding ODD, latency loops, compute constraints, and the modern Hybrid Architecture (Modular vs. End-to-End).
The raw senses of an autonomous vehicle: What data does each sensor provide? Covers cameras, radar, LiDAR, ultrasonics, and microphones—their physics, strengths, weaknesses, and why fusion is necessary.
If you don't know where your eyes are relative to your feet, you trip. Covers intrinsics, extrinsics, SE(3) transforms, online vs. offline calibration, and time synchronization.
From GPS to centimeter accuracy: How autonomous vehicles know their exact position. Covers GNSS, IMU, wheel odometry, scan matching, and Factor Graphs.
How autonomous vehicles remember the world. Covers HD maps, lane graphs, offline vs. online mapping, MapTR, and the map-heavy vs. map-light debate.
From pixels to 4D realities: How AVs understand their environment. Deep dive into BEV Transformers, Panoptic Occupancy, Scene Flow, and Foundation Models for open-world perception.
From perception to action: How autonomous vehicles make decisions. Covers cost functions, game-theoretic planning, MPC, and the "End-to-End" debate.
The hardest problem in AV: predicting human irrationality. From physics-based Kalman Filters to Joint Autoregressive Distributions, Generative Motion Diffusion, and World State Propagations.
A curated list of book recommendations covering personal development, philosophy, psychology, and life lessons from my personal library.
How to move from visual imitation to law-governed motion. Deep dive into injecting PDEs into neural networks, implicit physics extraction, and LLM-guided physical reasoning.
Part 4 of a comprehensive guide to agentic AI design patterns. Covers common failure modes, safety mechanisms, verifiable pipelines, and how to build reliable production systems.
Part 3 of a comprehensive guide to agentic AI design patterns. Covers specialized patterns: embodied agents, 3D scene understanding, imagination loops, multi-agent societies, error recovery, and self-debugging.
Part 5 of a comprehensive guide to agentic AI design patterns. Covers 2025 trends, cost optimization, case studies, production checklist, and the state of the field.
Part 1 of a comprehensive guide to agentic AI design patterns. Covers the fundamentals: ReAct loops, planning, tool use, self-consistency, and graph-based reasoning.
Part 2 of a comprehensive guide to agentic AI design patterns. Covers production-ready patterns: memory management, supervisor/orchestrator, parallel tool execution, and hidden reasoning.
An exploration of modern agent systems, with math, analogies, and examples. From ReAct loops to multi-agent societies, discover the design patterns that make AI agents think, act, and fix themselves.
An intuitive introduction to the Transformer architecture — from the attention mechanism to self-attention and cross-attention, using language translation as a concrete example.
An intuitive introduction to Variational Autoencoders — how compressing data into probabilistic codes enables machines to generate realistic images, sounds, and structures.
A deep dive into the physics and probability of diffusion models. Learn how reversing a stochastic process became the foundation for modern generative AI, from Stable Diffusion to robotics and protein design.
Reflections on building production-grade behavior prediction systems for autonomous vehicles — and why closed-loop reasoning is the bridge between perception and planning.
My research journey from wireless communication foundations to solving the camera calibration bottleneck that enables autonomous vehicle vision.
How we used deep learning to automatically calibrate traffic cameras by observing vehicle motion—work that won Best Paper Award at ACM BuildSys 2017.